Life Science

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of different cell types in vitro and in vivo.1-5 Although there are literally thousands of publications using such dyes, some of the most commonly encountered cell tracking applications include monitoring of:

  1. stem and progenitor cell quiescence, proliferation and/or differentiation6-8
  2. antigen-driven membrane transfer9 and/or precursor cell proliferation3,4,10-18 and
  3. immune regulatory and effector cell function1,18-21.

Commercially available cell tracking dyes vary widely in their chemistries and fluorescence properties but the great majority fall into one of two classes based on their mechanism of cell labeling. "Membrane dyes," typified by PKH26, are highly lipophilic dyes that partition stably but non-covalently into cell membranes1,2,11. "Protein dyes," typified by CFSE, are amino-reactive dyes that form stable covalent bonds with cell proteins4,16,18. Each class has its own advantages and limitations. The key to their successful use, particularly in multicolor studies where multiple dyes are used to track different cell types, is therefore to understand the critical issues enabling optimal use of each class2-4,16,18,24.

The protocols included here highlight three common causes of poor or variable results when using cell-tracking dyes. These are:

  1. Failure to achieve bright, uniform, reproducible labeling. This is a necessary starting point for any cell tracking study but requires attention to different variables when using membrane dyes than when using protein dyes or equilibrium binding reagents such as antibodies.
  2. Suboptimal fluorochrome combinations and/or failure to include critical compensation controls. Tracking dye fluorescence is typically 102 - 103 times brighter than antibody fluorescence. It is therefore essential to verify that the presence of tracking dye does not compromise the ability to detect other probes being used.
  3. Failure to obtain a good fit with peak modeling software. Such software allows quantitative comparison of proliferative responses across different populations or stimuli based on precursor frequency or other metrics. Obtaining a good fit, however, requires exclusion of dead/dying cells that can distort dye dilution profiles and matching of the assumptions underlying the model with characteristics of the observed dye dilution profile.

Examples given here illustrate how these variables can affect results when using membrane and/or protein dyes to monitor cell proliferation.

 

Materials

     

References

  1. Poon, R.Y., Ohlsson-Wilhelm, B.M., Bagwell, C.B., & Muirhead, K.A. Use of PKH Membrane Intercalating Dyes to Monitor Cell Trafficking and Function. In: Living Color: Flow Cytometry and Cell Sorting Protocols., Diamond, R.A. & DeMaggio, S., eds., Springer-Verlag, New York, NY, 302-352 (2000).
  2. Wallace, P.K. & Muirhead, K.A. Cell Tracking 2007: A Proliferation of Probes and Applications. Immunol. Invest. 36, 527-562 (2007).
  3. Hawkins, E.D., Hommel, M., Turner, M.L., Battye, F.L., Markham, J.F., & Hodgkin, P.D. Measuring lymphocyte proliferation, survival and differentiation using CFSE time-series data. Nat. Protoc. 2, 2057-2067 (2007).
  4. Quah, B.J., Warren, H.S., & Parish, C.R. Monitoring lymphocyte proliferation in vitro and in vivo with the intracellular fluorescent dye carboxyfluorescein diacetate succinimidyl ester. Nat. Protoc. 2, 2049-2056 (2007).
  5. Bolton, D.L., Minang, J.T., Trivett, M.T., Song, K., Tuscher, J.J., Li, Y., Piatak, Jr., M., O'Connor, D., Lifson, J.D., Roederer, M., & Ohlen, C. Trafficking, Persistence, and Activation State of Adoptively Transferred Allogeneic and Autologous Simian Immunodeficiency Virus-Specific CD8+ T Cell Clones during Acute and Chronic Infection of Rhesus MacaquesJ. Immunol. 184, 303-314 (2010).
  6. Juopperi, T.A. & Sharkis, S.J. Isolation of Quiescent Murine Hematopoietic Stem Cells by Homing PropertiesMeth. Mol. Biol. 430, 21-30 (2008).
  7. Kusumbe, A.P. & Bapat, S.A. Cancer stem cells and aneuploid populations within developing tumors are the major determinants of tumor dormancyCancer Res. 69, 9245-53 (2009).
  8. Pece, S., Tosonim, D., Confalonieri, S., Mazzarol, G., Vecchi, M., Ronzoni, S., Bernard, L., Viale, G., Pelicci, P.G., & Di Fiore, P.P. Biological and Molecular Heterogeneity of Breast Cancers Correlates with Their Cancer Stem Cell ContentCell. 140, 62-73 (2010).
  9. Gertner-Dardenne, J., Poupot, M., Gray, B.D., & Fournié, J.-J. Lipophilic fluorochrome trackers of membrane transfers between immune cellsImmunol. Invest. 36, 665-685 (2007).
  10. Bercovici, N., Givan, A.L., Waugh, M.G., Fisher, J.L., Vernel-Pauillac, F., Ernstoff, M.S., Abastado, J.P., & Wallace, P.K. Multiparameter precursor analysis of T-cell responses to antigenJ. Immunol. Methods.276, 5-17 (2003).
  11. Givan, A.L., Fisher, J.L., Waugh, M.G., Bercovici, N., & Wallace, P.K. Use of cell-tracking dyes to determine proliferation precursor frequencies of antigen-specific T cellsMethods Mol. Biol. 263, 109-24 (2004).
  12. Schwaab, T., Tretter, C.P., Gibson, J.J., Cole, B.F., Schned, A.R., Harris, R., Fisher, J.L., Crosby, N., Stempkowski, L.M., Heaney, J.A., & Ernstoff, M.S. Tumor-related immunity in prostate cancer patients treated with human recombinant granulocyte monocyte-colony stimulating factor (GM-CSF)Prostate. 66(6), 667-674 (2006).
  13. Bantly, A.D., Gray, B.D., Breslin, E., Weinstein, E.G., Muirhead, K.A., Ohlsson-Wilhelm, B.M., & Moore, J.S. CellVue Claret, a New Far-Red Dye, Facilitates Polychromatic Assessment of Immune Cell Proliferation. Immunol. Invest. 36, 581-605 (2007).
  14. Givan, A.L. A flow cytometric assay for quantitation of rare antigen-specific T-cells: using cell-tracking dyes to calculate precursor frequencies for proliferation. Immunol. Invest. 36, 563-580 (2007).
  15. Tario, J.D., Jr., Gray, B.D., Wallace, S.S., Muirhead, K.A., Ohlsson-Wilhelm, B.M., & Wallace, P.K. Novel lipophilic tracking dyes for monitoring cell proliferationImmunol Invest. 36, 861-885 (2007).
  16. Wallace, P.K., Tario, Jr. J.D., Fisher, J.L., Wallace, S.S., Ernstoff, M.S., & Muirhead, K.A. Tracking Antigen-Driven Responses by Flow Cytometry: Monitoring Proliferation by Dye Dilution. Cytometry. 73A, 1019-1034 (2008).
  17. Barth, R.J., Fisher, D.A., Wallace, P.K. Channon, J.Y., Noelle, R.L., Gui, J., & Ernstoff, M.S. A Randomized Trial of Ex vivo CD40L Activation of a Dendritic Cell Vaccine in Colorectal Cancer Patients: Tumor-Specific Immune Responses Are Associated with Improved SurvivalClin. Cancer Res. 16, 5548-5556 (2010).
  18. Tario, J.D., Jr., Muirhead, K.A., Pan, D., Munson, M., & Wallace, P.K. Tracking Immune Cell Proliferation and Cytotoxic Potential Using Flow CytometryMeth. Mol. Biol. 699, 119-164 (2011).
  19. Fuse, S. & Underwood, E. Simultaneous Analysis of In Vivo CD8+ T Cell Cytotoxicity Against Multiple Epitopes using Multicolor Flow CytometryImmunol. Invest. 36, 829-845 (2007).
  20. Schütz, C., Fleck, M., Mackensen, A., Zoso, A., Halbritter, D., Schneck, J.P., & Oelke, M. Killer artificial antigen-presenting cells: a novel strategy to delete specific T cellsBlood. 111, 3546-3552 (2008).
  21. Zaritskaya, L., Shurin, M.R., Sayers, T.J., & Malyguine, A.M. New flow cytometric assays for monitoring cell-mediated cytotoxicityExpert Rev. Vaccines. 9, 601-616 (2010).
  22. Roederer, M. Interpretation of cellular proliferation data: Avoid the panglossianCytometry79A, 95-101 (2011).
  23. Personal communication, E. Breslin (Zynaxis Cell Science, Inc.).
  24. Quah, B.J.C. & Parish, C.R. The Use of Carboxyfluorescein Diacetate Succinimidyl Ester (CFSE) to Monitor Lymphocyte ProliferationJ. Vis. Exp. (44), e2259, doi:10.3791/2259 (2010).
  25. Sigma product bulletin for PKH26.
  26. Houlihan, D.D. & Newsome, P.N. Critical Review of Clinical Trials of Bone Marrow Stem Cells in Liver DiseaseGastroenterology. 135, 438-450 (2008).
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